System identification is the task of constructing representative
models of processes and has become an invaluable tool in many
different areas of science and engineering. Due to the inherent
complexity of many real world systems the application of
traditional techniques is limited. In such instances more
sophisticated (so called intelligent) modelling approaches are
required. Neurofuzzy modelling is one such technique, which by
integrating the attributes of fuzzy systems and neural networks
is ideally suited to system identification. This attractive
paradigm combines the well established learning techniques of a
particular form of neural network i.e.\ generalised linear models
with the transparent knowledge representation of fuzzy systems,
thus producing models which possess the ability to learn from
real world observations and whose behaviour can be described
naturally as a series of linguistic humanly understandable rules.
Unfortunately, the application of these systems is limited to
low dimensional problems for which good quality expert knowledge
and data are available.

The work described in this thesis addresses this fundamental
problem with neurofuzzy modelling, as a result algorithms which
are less sensitive to the quality of the {\em a priori} knowledge
and empirical data are developed. The true modelling
capabilities of any strategy is heavily reliant on the model's
structure, and hence an important (arguably the most important)
task is structure identification. Also, due to the {\em curse of
dimensionality}, in high dimensional problems the size of
conventional neurofuzzy models gets prohibitively large. These
issues are tackled by the development of automatic neurofuzzy
model identification algorithms, which exploit the available
expert knowledge and empirical data. To alleviate problems
associated with the curse of dimensionality, aid model
generalisation and enhance model transparency, parsimonious
models are identified. This is achieved by the application of
additive and multiplicative neurofuzzy models which exploit
structural redundancies found in conventional systems.

The developed construction algorithms successfully identify
parsimonious models, but as a result of noisy and poorly
distributed empirical data, these models can still generalise
inadequately. This problem is addressed by the application of
Bayesian inferencing techniques; a form of regularisation.
Smooth model outputs are assumed and superfluous model
parameters are controlled, sufficiently aiding model
generalisation and transparency, and data interpolation and
extrapolation. By exploiting the structural decomposition of the
identified neurofuzzy models, an efficient local method of
regularisation is developed.

All the methods introduced in this thesis are illustrated on
many different examples, including simulated time series, complex
functional equations, and multi-dimensional dynamical systems.
For many of these problems conventional neurofuzzy modelling is
unsuitable, and the developed techniques have extended the range
of problems to which neurofuzzy modelling can successfully be
applied.